Research

Every year, Americans elect hundreds of thousands of candidates to local public office, typically in low-attention, nonpartisan races. How do voters evaluate candidates in these sorts of elections? Previous research suggests that, absent party cues, voters rely on a set of heuristic shortcuts—including the candidate’s name, profession, and interest group endorsements—to decide whom to support. In this paper, we suggest that community embeddedness—a candidate’s roots and ties to the community—is particularly salient in these local contests. We present evidence from a conjoint survey experiment on a nationally representative sample of American voters. We estimate the marginal effect on vote share of candidate attributes such as gender, race, age, profession, interest group endorsements, and signals of community embeddedness— specifically homeownership and residency duration. We find that voters, regardless of political party, have strong preferences for community embeddedness. Strikingly, the magnitude of the residency duration effect rivals that of prior political experience.

Paper

This paper examines the extent to which social pressures can foster greater responsiveness among public officials. I conduct a non-deceptive field experiment on 1400 city executives across all 50 states and measure their level of responsiveness to open records requests. I use two messages to prime social pressure. The first treatment centers on the norm and duty to be responsive to the public’s request for transparency. The second treatment is grounded in the peer effect literature, which suggests that individuals change their behavior in the face of potential social sanctioning and accountability. I find no evidence that mayors are affected by priming the officials’ duty to the public. The mayors who received the peer effects prime were 6–8 percentage points less likely to respond, which suggests a “backfire effect.” This paper contributes to the growing responsiveness literature on the local level and the potential detrimental impact of priming peer effects.

Link to paper here.

Urban–rural differences in partisan political loyalty are as familiar in the United States as they are in other countries. In this paper, we examine Gallup survey data from the early-2000s through 2018 to understand the urban–rural fissure that has been so noticeable in recent elections. We consider the potential mechanisms of an urban–rural political divide. We suggest that urban and rural dwellers oppose each other because they reside in far apart locations without much interaction and support different political parties because population size structures opinion quite differently in small towns compared with large cities. In particular, we consider the extent to which the compositional characteristics (i.e., race, income, education, etc.) of the individuals living in these locales drives the divide. We find that sizable urban–rural differences persist even after accounting for an array of individual-level characteristics that typically distinguish them.

Link to the paper here.

Link to Oxford Bibliographies here.

Link here.

Given a patchwork system of overlapping local institutions, can residents direct public policy? Current approaches to representation at the local level may present a distorted view of how democracy operates because they fail to account for the overlapping nature of institutions. To address this gap, I first implement a framework that incorporates multiple overlapping governing institutions: cities, counties, school districts, and special districts. Second, I use data from more than 500,000 survey responses to estimate a novel measure of local ideological preferences for cities over time. Finally, to assess the impact of ideology on public policy outcomes, I use a Bayesian within-between random effects model. This methodology yields three major findings. First, I demonstrate that cross-sectional responsiveness exists. Second, I find evidence for dynamic responsiveness in spending but inconclusive evidence for taxation. Third, I provide descriptive evidence that consolidated governance fosters greater responsiveness. I reframe the responsiveness discussion from a single governing unit to a holistic system of overlapping institutions and provide the strongest evidence to date that local governments respond dynamically to the ideology of citizens.

Link to Draft as of 9/24/23

Link to PolMeth 2021 Poster

Where and why are discriminatory ordinances adopted? Theories of racial threat suppose that members of a racial majority group regard the presence of minorities as a threat to their socio-political status and implement policies to hurt that minority population. I use the racial threat hypothesis to examine the adoption of criminal activity nuisance ordinances (or crime-free housing laws). These ordinances allow officials to designate specific properties and residents as nuisances after repeated police interactions. After that designation, property owners are penalized with fines or the seizure of property if they do not respond by removing the residents. Using data from Ohio municipalities, I find that the racial composition of cities predicts the emergence of criminal activity nuisance ordinances. I attempt to rule out alternative hypotheses surrounding the proportion of renter-occupied housing, crime, and poverty. In further exploring the results, I use a machine learning technique called Random Forests to uncover the discontinuity or “tipping point” where the propensity for adopting such a policy sharply increases or decreases. This research speaks to the generalizability of the racial threat hypothesis, the importance of representation, and the nation's diversification.

Link to PolMeth 2022 Poster
Link to Paper as of 10/14/24

Regression discontinuity designs have risen to prominence in the social sciences because they enable the estimation of local average treatment effects under a few assumptions. However, given the natural variation in the data, some empirical applications are more plausible than others. The most convincing discontinuities are ones that readers can clearly visualize a priori. We propose integrating Bayesian change point analysis (CPA) within the RD design framework to visualize and compare discontinuities to the natural variation in the data. Using this approach, researchers may efficiently identify and evaluate discontinuities in the raw data without pre-specification. The ability of CPA to correctly identify the theory-driven discontinuity should lend confidence to the results, while finding other discontinuities may signal noisy data or threats to identification, such as anticipation. Through simulations and empirical applications, we demonstrate how CPA can assist in visualizing data and validating breakpoints against theoretical expectations. We leave practitioners with a sequential workflow to aid their research using regression discontinuity designs.

Paper (Updated September 1, 2024)

Slides (Updated July 17, 2024)

Poster: Asian and MENA Polmeth

Note: The paper is in development, but if you have comments and suggestions, please send them to bryant.moy@nyu.edu. Thanks!

Does instilling external oversight over policing result in enhanced public perceptions of police legitimacy? Civilian review boards (CRBs) are frequently promoted as mechanisms to enhance the legitimacy of police agencies by providing independent oversight. Despite public support for CRBs, their adoption and effectiveness remain limited, raising concerns about their actual impact on procedural fairness and police legitimacy. This study assesses the role of CRBs in shaping public perceptions by examining various decision-making scenarios involving police chiefs and CRBs. Using a survey experiment fielded to 2,503 respondents, we investigate whether CRBs enhance legitimacy when they either coincide with or conflict with police chiefs' determinations in cases of officer misconduct. Our findings suggest that while CRBs may enhance perceptions of procedural fairness for some, particularly those with negative views of police, their involvement does not universally increase legitimacy. In fact, when CRBs conflict with police chiefs, they may diminish public trust in both policing and civilian oversight. These results provide empirical evidence to support concerns that CRBs might not fulfill their intended role in enhancing police legitimacy, especially in cases of institutional disagreement.

Does ideology operate in similar ways at the national and local levels? The current debate about the existence of responsiveness at the local level rest on the answer to this descriptive question. Previous studies suggest that non-ideological factors are the main drivers of local politics, while more recent research has highlighted the impact of mass/aggregate ideology on local government policies. However, the relationship between ideology and local governance is not well understood, as previous research may have conflated local-level preferences (national views disaggregated to the local level) with local-government preferences (attitudes about cities, counties, and school districts). This short article examines the relevance of self-placement ideology to local politics research. To address this issue, we conducted a survey of Americans to assess how self-placement ideology reflects individual attitudes on local taxation and spending at various levels of government (city, county, school district, and federal). We explicitly target a series of descriptive estimand of the observed ideology of individuals and their support for fiscal policy across levels of government. Our results indicate that self-placement ideology is a measure of residents' general preferences on taxation and spending, applicable across levels of government.

Social scientists have long recognized that unrepresentative samples limit our ability to draw conclusions about target populations. Nevertheless, researchers routinely employ Regression Discontinuity Designs (RDD) in applied causal inference work without characterizing who comprises their analytical sample. Although researchers explicitly acknowledge that RDD results are only identified near the cutoff, they rarely provide a discussion analyzing whom this effect is estimated upon. This oversight has significant implications: we cannot assess whether findings generalize beyond the cutoff, we cannot improve theory development, and we may provide misguided policy recommendations. We propose a novel framework for characterizing RDD analytical samples and comparing them against the broader study population or other target populations of interest. Our approach involves computing covariate means weighted by distance to the cutoff and providing researchers with graphical tools to understand who contributes to their estimated effects. We demonstrate our method's utility through empirical applications and provide an R package for implementation. Our project highlights the importance of description in causal inference work.

Poster - Asian PolMeth 2025

Historical housing discrimination, particularly through racially restrictive covenants, continues to shape racial inequality in the United States. Despite this legacy, public support for corrective policies remains limited. This paper examines whether combining historical information about housing discrimination with visual representation can increase support for reparative policies. Through a pilot survey experiment of 498 white respondents, we randomize participants into seven conditions varying both informational content about housing policies and visual representation through family images. While information alone about racially restrictive covenants shows minimal effect on policy attitudes, we find that the same information paired with images of Black families significantly increases support for reparative policies. Notably, this effect does not emerge when the same information is paired with images of white families, suggesting that visual representation of affected communities plays a crucial role in translating historical knowledge into policy support. These findings contribute to our understanding of effectively communicating historical injustices and building support for remedial policies.

[Preliminary results are from a pilot study]

Why do some ethnic groups produce local political leaders while others do not? While previous research emphasizes demographic representation and general segregation patterns, we argue that the spatial distribution of ethnic groups within cities plays a role in candidate emergence. We theorize that ethnic enclaves - geographic clustering of co-ethnic populations and supportive institutions - facilitate political leadership by reducing mobilization costs, enabling claims for targeted public goods, and fostering dense social networks. Ethnic enclaves differ from general patterns of segregation by combining geographic clustering in space with self-sustaining institutions, businesses, and social networks. To test this theory, we examine white ethnic enclaves in U.S. cities, developing a novel measurement approach that combines machine learning classification of candidates' ethnic ancestry with a spatial measure of ethnic enclaves. Using data from 638 cities over five decades, we find that higher levels of ethnic clustering significantly increase both the emergence and electoral success of co-ethnic candidates, particularly in city council races. This effect persists after controlling for demographic share and is robust across multiple specifications. Importantly, this relationship is nonlinear -- the effect of clustering becomes most pronounced once ethnic groups reach a threshold level of spatial concentration. In addition, the relationship is especially pronounced when ethnic groups exhibit strong internal social ties, suggesting the importance of community networks. Our findings demonstrate that spatial concentration, beyond simple population share, shapes pathways to local political leadership. This research contributes to our understanding of ethnic politics and local representation by highlighting how geographic clustering influences political mobilization and candidate emergence.

Discriminatory police practices have prompted calls to defund the police. While the definition of `defund' encompasses various proposals, the diversion of police funding or the failure to hire officers has broad consequences. Indeed, recent work by economists find that increasing police force size saves Black lives by reducing homicides. However, increases in police on the streets also increases the enforcement and arrests for low-level quality-of-life offenses, which disproportionately fall on Black Americans and triggers perceptions of undue surveillance and targeting. This under-over-policing phenomenon may explain the lack of public support for defunding proposals, even among Black Americans. This paper employs an experimental survey design to investigate how information regarding the consequences of policing (reduction in homicide rates and increase in low-level quality-of-life arrests) influences the public's perception and willingness to support more police on the streets. We expect Blacks and Whites to process these tradeoffs differently. This project speaks to the broad challenges of policing in unequal multiracial democracies and the potential policy tradeoffs cities must consider when funding the police.

We are currently developing a nonparametric Bayesian approach — Gaussian Process Regressions — to estimate small-area public opinion from large nationally representative surveys.

The mayoral email archive is a novel dataset that includes non-private emails archived from the inboxes and sent boxes of mayors between January 1, 2018, and March 31, 2018. I collected these emails via a series of open records requests sent out in the summer of 2018. The open records requests were initially a part of a project on social pressure and mayoral responsiveness that was published in the Journal of Experimental Political Science in 2021.

I am currently downloading, parsing, and cleaning the email records. After the cleaning process is complete, I will incorporate the emails into my research agenda on local governance.